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为搭建我校博士后学术交流平台，促进学术水平提升，学校博士后管理办公室组织开展博士后学术沙龙活动。本次沙龙由我校博士后Abdul Basit、李晶晶、Memon Muhammad Hammad、袁召全和张爱娟分享其研究成果，诚挚邀请感兴趣的师生参加。
一、时 间：2018年6月27日（周三）14:00
二、地 点：清水河校区经管楼宾诺咖啡
三、主办单位：电子科技大学博士后管理办公室
承办单位：计算机科学与工程学院（网络空间安全学院）
电子科技大学博士后联谊会
四、活动安排：
报告一：
（1）主 题：Cognitive Frequency Diverse Array: Application in navigation and joint radar-communication systems.
（2）主讲人：Abdul Basit 信息与通信工程学院博士后
（3）交流内容：Cognitive radar (CR) based beamforming is different than that of a conventional radar in a sense that it utilizes a feedback to connect the receiver to the transmitter for intelligently adjusting the design parameters. CR literature can be divided into two dissimilar types, namely, exterior, where cognition source information is gathered from geographical maps etc., and the interior, where the target next state is predicted by learning the hidden knowledge through observations to intelligently adjust the transmit parameter to achieve an improved performance. Since cognitive beamforming is one of methods in hand for efficient utilization of the radar system resources, it has been applied towards frequency diverse array radar. Despite of an FDA improved target detection and localization performance, energy focusing towards a desired target range-angle sector for the whole pulse width is a major problem to be addressed. Moreover, joint cognitive radar and communication designs are the need of the time due to the increased desire of efficient radio frequency (RF) spectrum utilization, while reducing the installation plus hardware costs at the same time. Precisely, the co-existence of these two designs hybridized with cognition at a single platform may invoke new research areas for efficiently searching and utilizing the vacant RF spectrum without affecting the radar performance. Moreover, spectrum sharing of radar and Wi-Fi networks is also feasible. There is thus a large area of applications, which would possibly benefit from the availability of cognitive joint radar-communication systems.
（4）主讲人简介：Abdul Basit received the B.S. Degree in 2007 form Muhammad Ali Jinnah University, Islamabad, Pakistan. Afterwards he received M.S and PhD degrees from International Islamic University, Islamabad, Pakistan in 2009 and 2016, respectively. He joined school of information and communication engineering, UESTC as a post-doc researcher in OCT 2017. His areas of interests include cognitive beamforming, frequency diverse array, phased array and MIMO radars.
报告二：
（1）主 题：Domain adaptation via feature mapping and landmark selection
（2）主讲人：李晶晶 计算机科学与工程学院（网络空间安全学院）博士后
（3）交流内容：In the big data era, data management is becoming increasingly challenging because endless stream of novel applications and corresponding user data are enormously generated in every single second. Accurately classifying, or labeling, these data is the prerequisite for other operations, such as class-wise indexing and retrieving. However, a new application also means that we have insufficient training samples to train an accurate classifier. What should one do in this situation? Well-labeled data are always scarce in classification tasks, especially in new domains, and it is unwise to think that we should all start from scratch. As a result, domain adaptation has been proposed for the purpose of borrowing knowledge from related domains. Typically, domain adaptation deals with the problem where a well-labeled source domain and an unlabeled target domain are involved, and the two domains have divergent probability distributions. It aims to leverage the label information in the source domain, so that the task in the target domain can be solved by knowledge transfer. As a practical branch of transfer learning, domain adaptation has been exploited in many fields, e.g., image classification, objection recognition, text categorization and video event detection.
This talk will give a brief introduction of transfer learning and domain adaptation. Specifically, domain adaptation via feature mapping and landmark selection will be discussed.
（4）主讲人简介：Jingjing Li received his MSc and PhD degree in Computer Science from University of Electronic Science and Technology of China in 2013 and 2017, respectively. He was a visiting student in The University of Queensland from 2016 to 2017. Now he is a national Postdoctoral Program for Innovative Talents research fellow with the School of Computer Science and Engineering, University of Electronic Science and Technology of China. He has great interest in machine learning, especially transfer learning, subspace learning and recommender systems.
报告三：
（1）主 题：IMRBS: image matching for location determination through a region-based similarity technique for CBIR
（2）主讲人：Memon Muhammad Hammad 计算机科学与工程学院（网络空间安全学院）博士后
（3）交流内容：This paper presents a technique for content-based image retrieval (CBIR) by selecting the regions on the basis of their contribution to image contents. We have analyzed the problems associated with matching regions among the pair of images over the large set of overlapping regions. It is being studied that matching images by using regions having unstructured association can be a serious problem. In this research, we propose a linear formulation technique, which involves simultaneous matching, so that the matched area can have color-similarity histogram, shape and having little overlapping region. It is also analyzed that the selected region can have a small number and overall concavity is low, and tried to cover both the images. It has been studied that CBIR has attracted many researchers and most of the previous CBIR systems have shown the searching procedure of the digital image on the basic features such as texture, color, size, and shape of a certain query image in a large database. According to this research, we are going to present a region-based image repossession system that is going to exhibit a model that would help specify multiple regions of interest inside the query image. In this research, we have presented a novel visual feature that might contain color size of the region query and its moments, however, to combine color and region-size information of the watershed region. Moreover, a technique has been modeled for region filtering that might depend upon the color size of the given query image and that would stimulate the process of screening out the most nonrelated region and images for pre-processing of the recovery of image. Therefore, the technique presented would help shorten the consequence of image background on image-matching decision; however, an object’s color would receive much more focus. Apart from that, amendment to region-based similarity measurement has also been presented. It has been proved with the help of simulation results that the given descriptor with the similarity measure amendments outperforms the existing descriptor that would be considered in content-based image-retrieval applications. Moreover, the given approach has performed better than the previous approaches. Our method would be based upon a simple and reliable metrics, which is being used to calculate similarities. We have performed numerous simulations and verified that the given approach has outperformed the current techniques in localization, and is going to have vigorous object discovery in the existing mixed-class dataset.
（4）主讲人简介：Muhammad Hammad Memon from Pakistan he is currently working as a Post-Doctoral Researcher at the School of Computer Science and Engineering, UESTC.He has done bachelor degree in commerce from University of Sindh, Jamshoro in the year 2009. He has Master of Engineering in Computer Science and Engineering at UESTC in the year 2014 and also from there he has also awarded his Ph. D. in the year of 2017. In Ph.D. he has studied the problem related to the field of Image Processing and Wavelet Transformation. During his study he has awarded with several excellent prestigious awards such as Academic Achievement award 2015-2016 and Excellent Performance Award in the academic year 2014-2015 as well as in 2015-2016 by UESTC. Also he has awarded as Chinese Government Outstanding International Student Scholarship, China for the year 2016 sponsored by China Scholarship Council, Beijing. Moreover Dr. Memon has awarded Outstanding International Student of UESTC-2017 as well as Outstanding Student of UESTC-2017. However he has also worked as a country representative of Pakistan in UESTC for 2016-2017 and worked as Social Media Administrator and Team Leader (TV station) for School of International Education, UESTC for 2015-2016 awarded for outstanding contribution as Vice President, International Students Union, Social Events for 2014-2015 as well as for 2015-2016 by UESTC. Dr. Memon has also Professional Certifications in Computer Science, Networking and Business Management from USA, UAE and Pakistan. He has published over 23 research papers in recent years. He is also associate editor IEEE Access and various reputed international journals. His research interests include in the areas of artificial intelligence, network security, cloud computing, image processing and CBIR techniques etc.
报告四：
（1）主 题：Joint Perception Learning and Causal Reasoning for Motivation Understanding in Images
（2）主讲人：袁召全 计算机科学与工程学院（网络空间安全学院）博士后
（3）交流内容：Understanding potential motivations behind people’s actions in images is a key research topic in the computer vision and multimedia. It is a very challenging, because motivations are usually beyond plain image pixels and hard to be described visually. To solve this task, we employ high-level textual information and explore a potential causal structure among the concepts of scenes, actions and motivations. Specifically, we propose a novel joint perception learning and causal reasoning method (referred to as PLCR), where the textual data associated with images are utilized for building the causal structure, based on which the perception modules are learned jointly. Unlike most existing visual recognition models, PLCR infers the motivations by executing perception learning and causal reasoning seamlessly. Comprehensive experiments are conducted on the Motivations dataset clearly show the effectiveness of our proposed method.
（4）主讲人简介：Zhaoquan Yuan received the B.E. degree in computer science and technology from the University of Science and Technology of China, Hefei, China, in 2006, and the Ph.D. degree from the Chinese Academy of Sciences, Beijing, China, in 2015. He currently works as a postdoctoral fellow in School of Computer Science and Engineering (CSE) of UESTC, China. His research interests include machine learning, causal inference, and multimedia content analysis.
报告五：
（1）主 题：Twin Engineering in Solution-Synthesized Nonstoichiometric Cu5FeS4 Icosahedral Nanoparticles for Enhanced Thermoelectric Performance.
（2）主讲人：张爱娟 物理学院博士后
（3）交流内容：Thermoelectrics, capable of directly converting thermal energy into electric energy, can be utilized to generate useful electricity from waste heat, thus possessing potential in alleviating the energy demands. Defect engineering has proven effective to optimize the thermal and electronic properties of a diversity of thermoelectric materials. Therefore, twin engineering provides a potential strategy to synergistically optimize the thermal and electrical transport properties.
In this report, a facile colloidal solution method has been developed for the fast, scalable synthesis of orthorhombic@cubic core–shell nonstoichiometric Cu5FeS4 icosahedral nanoparticles. Such nanoparticles contain high-density twin boundaries in the form of five-fold twins. Spark plasma sintering consolidates the nanoparticles into nanostructured pellets, which retain high-density twin boundaries and a tuned fraction of the secondary phase Fe-defcient cubic Cu5FeS4. As a result, the thermal and electrical transport properties are synergistically optimized, leading to an enhanced zT of ≈0.62 at 710 K, which is about 51% higher than that of single-phase Cu5FeS4. This study provides an energy-effcient approach to realize twin engineering in nonstoichiometric Cu5FeS4 nanomaterials for high-performance thermoelectrics.
（4）主讲人简介：A. Zhang received the Ph.D. degree in College of Physics of Chongqing University with research in Condensed Matter Physics. She is currently a postdoctoral research fellow with the School of Physics, UESTC. She is interested in syntheses of nanocrystals for application in thermoelectric and energy storage.
电子科技大学博士后管理办公室
2018年6月25日
编辑：林坤 / 审核：李果 / 发布：陈伟
